Upload run_quick_test.py

run_quick_test.py

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+"""Test rapide et léger du système MLE."""
+import sys
+sys.path.insert(0, '.')
+
+import numpy as np
+import time
+from mle.mle_system import MLESystem
+from mle.memory import VECTOR_SIZE
+
+np.random.seed(42)
+
+print("="*60)
+print("MLE QUICK TEST")
+print("="*60)
+
+mle = MLESystem(memory_capacity=500, online_learning=True, temperature=0.5)
+
+# Phase 1: Crée des vecteurs reliés (concepts)
+print("\n--- Creating related concepts ---")
+n_concepts = 3
+n_variants = 3
+
+concepts = []
+for c in range(n_concepts):
+    base_active = np.random.choice(VECTOR_SIZE, size=200, replace=False)
+    base = np.zeros(VECTOR_SIZE, dtype=np.uint8)
+    base[base_active] = 1
+    
+    variants = []
+    for v in range(n_variants):
+        variant = base.copy()
+        # Ajoute du bruit
+        to_flip = np.random.choice(VECTOR_SIZE, size=30, replace=False)
+        variant[to_flip] = 1 - variant[to_flip]
+        variants.append(variant)
+    
+    concepts.append((base, variants))
+    
+    # Traite les variants
+    for variant in variants:
+        t0 = time.time()
+        result = mle.process(variant)
+        t1 = time.time()
+        
+        if result.energy_trajectory:
+            print(f"  Concept {c}, variant: energy={result.energy_trajectory[-1]:.0f}, "
+                  f"conv={result.converged}, time={(t1-t0)*1000:.0f}ms")
+
+print(f"\nMemory after concepts: {mle.memory.size}")
+
+# Phase 2: Test généralisation avec requêtes bruitées
+print("\n--- Testing generalization (noisy queries) ---")
+for c, (base, _) in enumerate(concepts):
+    query = base.copy()
+    # Plus de bruit
+    to_flip = np.random.choice(VECTOR_SIZE, size=80, replace=False)
+    query[to_flip] = 1 - query[to_flip]
+    
+    result = mle.process(query)
+    if result.energy_trajectory:
+        print(f"  Query concept {c}: energy={result.energy_trajectory[-1]:.0f}, "
+              f"conv={result.converged}")
+
+# Phase 3: Test stabilité
+print("\n--- Testing stability (continuous stream) ---")
+energies = []
+for i in range(20):
+    c = i % n_concepts
+    _, variants = concepts[c]
+    vec = variants[i % len(variants)]
+    
+    result = mle.process(vec)
+    if result.energy_trajectory:
+        energies.append(result.energy_trajectory[-1])
+    
+    if i % 5 == 0:
+        print(f"  [{i:2d}] memory={mle.memory.size}, n_assoc={len(mle.energy.associations)}")
+
+if len(energies) >= 10:
+    early = np.mean(energies[:5])
+    late = np.mean(energies[-5:])
+    print(f"\n  Early energy: {early:.0f}")
+    print(f"  Late energy:  {late:.0f}")
+    if late < early * 0.95:
+        print("  ✓ System is LEARNING")
+    else:
+        print("  ~ System is STABLE")
+
+# Phase 4: Test binding
+print("\n--- Testing binding ---")
+a = np.zeros(VECTOR_SIZE, dtype=np.uint8)
+a[np.random.choice(VECTOR_SIZE, size=200, replace=False)] = 1
+b = np.zeros(VECTOR_SIZE, dtype=np.uint8)
+b[np.random.choice(VECTOR_SIZE, size=200, replace=False)] = 1
+
+bound = mle.binder.bind(a, b)
+recovered = mle.binder.unbind(bound, a)
+sim = np.mean(recovered == b)
+print(f"  Binding similarity: {sim:.3f}")
+
+# Résumé
+print("\n" + "="*60)
+print("SUMMARY")
+print("="*60)
+mle.print_summary()
+
+print("\n✓ Quick test complete!")